AI doesn’t “make diagnoses” the way people think of it. But in real-world settings, automated tools can still influence the process—especially when busy providers are using decision support, imaging triage, lab interpretation aids, or risk stratification.
In Dana Point, common scenarios we see residents describe include:
- Delayed recognition of abnormal test results after an urgent care or follow-up referral.
- Imaging workflow issues (for example, a radiology review routed through automated prioritization) leading to a missed or downplayed finding.
- Inconsistent documentation between visits—where symptoms, history, or severity were not captured clearly enough to trigger escalation.
- Overreliance on “probable” outputs from clinical decision support systems, without adequate verification against objective findings.
Whether the error involved a clinician, a facility, a lab partner, or an automated workflow, the legal question is the same: did the care team meet the standard of care, and did the deviation contribute to harm?


